On Forms of Objects

Jadacki, Jacek

doi:10.1007/978-94-017-2990-1_13

Cited by 1 publication

(1 citation statement)

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“…[11]). While there have been studies of what representations are close to human perception following Gestalt psychology principles [16] [17], it has not been established how the image representations map towards discriminating artists and to higher-level semantic descriptions. We explore the search space of the above image representations and choose the one with highest accuracy.…”

Section: Sampling Image Representation Dimensionmentioning

confidence: 99%

Discovering salient characteristics of authors of artworks

Bajcsy

Moslemi

2010

SPIE Proceedings

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We addressed the problem of finding salient characteristics of artists from two-dimensional (2D) images of historical artifacts. Given a set of 2D images of historical artifacts by known authors, we discovered what salient characteristics made an artist different from others, and then enabled statistical learning about individual and collective authorship. The objective of this effort was to learn what would be unique about the style of each artist, and to provide the quantitative results about salient characteristic. We accomplished this by exploring a large search space of low level image descriptors. The motivation behind our framework was to assist humanists in discovering salient characteristics by automated exploration of the key image descriptors. By employing our framework we had not only saved time of art historians but also provided quantitative measures for incorporating their personal judgments and bridging the semantic gap in image understanding. We applied the framework implementation to the face illustrations in Froissart's Chronicles drawn by two anonymous authors. We reported the salient characteristics to be (HSV, histogram, k-nearest neighbor) among the 55 triples considered with 5-fold validations. These low level characteristics were confirmed by the experts to correspond semantically to the face skin colors.

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Section: Sampling Image Representation Dimensionmentioning

confidence: 99%